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KAUST Leverages Mixed Precision for Geospatial Data By Oliver Peckham July 28, 2021 For many computationally intensive tasks, exacting precision is not necessary for every step of the entire task to obtain a suitably precise result. The alternative is mixed-precision computing: using high precision where it matters, using low precision where it doesn’t as much. Now, a research team led by the King Abdullah University of Science and Technology (KAUST) has applied mixed-precision computing to drastically reduce the computational load of large geospatial dataset modeling. “For decades, modeling of environmental data relied on double-precision arithmetic to predict missing data,” explained Sameh Abdulah, a research scientist at KAUST and first author of the paper. “Today, there is high-performance computing hardware that can run single- and half-precision arithmetic with a speedup of 16 and 32 times compared with double-precision arithmetic. To take advantage of this, we prop

Sameh abdulahUniversity of tennessee knoxvilleKing abdullah university of scienceComputing research centerKing abdullah universityParallel runtime schedulingExecution controllerTennessee knoxvilleபல்கலைக்கழகம் ஆஃப் டென்னசி நாக்ஸ்வில்லேகிங் அப்துல்லா பல்கலைக்கழகம் ஆஃப் அறிவியல்கணினி ஆராய்ச்சி மையம்கிங் அப்துல்லா பல்கலைக்கழகம்டென்னசி நாக்ஸ்வில்லே